"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__:List[Any]									=			{
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Dict									=			[
       """RagModel""",
       """RagPreTrainedModel""",
       """RagSequenceForGeneration""",
       """RagTokenForGeneration""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[Any]									=			[
       """TFRagModel""",
       """TFRagPreTrainedModel""",
       """TFRagSequenceForGeneration""",
       """TFRagTokenForGeneration""",
   ]


if TYPE_CHECKING:
   from .configuration_rag import RagConfig
   from .retrieval_rag import RagRetriever
   from .tokenization_rag import RagTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rag import (
          TFRagModel,
          TFRagPreTrainedModel,
          TFRagSequenceForGeneration,
          TFRagTokenForGeneration,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
